Image moving target detection is an important research direction of the intelligent video surveillance field, and background modeling as a method most widely used in the motion detection is a critical part in the computer vision, and effective segmentation between background and prospect has significant influence on the following high layer processing such as target tracking, object identification and behavioral classification and so on. However, the background modeling method is comparatively sensitive to scene illumination changes, including long-term regular changes such as leaves trembling and day-night alternation and short-term random changes such as weather and moving objects and so on.
At present, the study of background modeling is mainly carried out from two aspects, i.e., pixel time domain statistics and spatial domain texture analysis, the method based on of pixel time domain distribution statistics, such as a Gaussian Mixture Model and an improved method thereof, can better adapt to the long-term regular illumination changes, however, since the model assumes that all pixels in a time series observed are mutually independent and very sensitive to subtle illumination or short-term illumination changes, the detection effect is not ideal enough. Based on the spatial domain background model such as a Local Binary Pattern (LBP), a Radial Reach filter has a good robustness under the short-term illumination changes since unchanged spatial domain textural features within local areas are used, however, if only part of pixels are changed within the local areas, the extracted features cannot satisfy the spatial domain invariance at this point, the detection effect will be affected greatly, and this situation tends to be more common in outdoor videos.
Compared with the LBP, a Center Symmetric-Local Binary Pattern (CSLBP) texture model in the related art has a lower feature dimension and a stronger anti-noise ability and is easy for real-time operations when applied in the background modeling. However, the CSLBP is confined to considering the robustness to the short-term illumination changes and fails to consider that time domain distribution characteristics of textures have significant influence on the background modeling, and when the long-term illumination changes cause that part of pixels within the local areas are changed, the CSLBP cannot satisfy the spatial domain invariance, thus in a scenario with complicated illumination changes, especially when short-term illumination changes and long-term illumination changes coexist, the CSLBP cannot satisfy needs of the background modeling.